Market Position and Competitive Analysis Dashboard

Context

ASOS finds itself in a highly competitive online fashion sector where customers can effortlessly compare prices and products of competing brands such as H&M in no time, frequently from their mobile phones. The research posed a challenging question: is ASOS' combination of prices and categories gaining its competitive advantage or rather silently leading it to be viewed as too pricey in the most important areas of the range? The study by using volume of products in each price range, the real breadth of assortment compared to H&M, and categories that are almost pure exclusive of high-priced products, the analysis not only helps ASOS and its stakeholders to identify the positions that current decisions may be taking in terms of margin risk, value perception, or lack of growth opportunities.

Insights

• ASOS presents a nearly two-fold product range in its online store compared to H&M, thus, not only fighting the battle of brands but also of the product assortment.
• ASOS has a catalogue that predominantly comprises of mid and upper price bands (£25–£75), whereas H&M's range is mostly in the lowest £0–£25 band.
• ASOS has some of its categories like ASOS EDITION lines and selected partner brands almost entirely consisting of high price products.
• Within overlapping price bands, ASOS is frequently closer to the upper end of the band than H&M, which confirms a more premium positioning within the same tiers.
• An independent samples t test comparing the lower size band (mean price 43.83, n = 2,912) with the higher size band (mean price 57.06, n = 1,329) showed a highly significant difference in average price between the two groups, t=-11.68, p≈1.0×10^(-30), indicating that the higher sizes are priced substantially higher on average.

Recommendations

• Launch products with lower prices at the entry-level in premium heavy, high volume ASOS categories as a strategy to keep budget-sensitive customers loyal.
• In the areas where ASOS is wider and a bit pricier than H&M, make it known that you are offering superior design, quality, and branding instead of just lowering the prices.
• Consider the high price driver categories pointed out in the visuals as a priority list for detailed range and pricing review.
• Keep an eye on the lowest price range, where H&M is the strongest, to prevent ASOS from being seen as too expensive in basic core items.

Methods

• The public product catalogues of ASOS and H&M were downloaded from Kaggle and after the cleaning of brand names, prices and category labels, the two joinable datasets were merged into one.
• Python (pandas and numpy) was the tool for data processing, which also included filtering of the data to exclude non-online products, and the standardisation of currencies and price fields.
• The methodology comprised of descriptive analytics as the main approach to present the product distribution by price bands and categories, and an comparison between price and assortment breadth of ASOS and H&M.
• Diagnostic analytics was also applied to understand the reasons for the observed patterns, for instance by sourcing the particular categories and brands that lead to ASOS’s price exposure and within band premium positioning.
• Then prescriptive insights are extracted from the patterns, which indicate ASOS where to do the opening price points adjustment, to rebalance the ranges with heavy premium or better to lean towards differentiation rather than discounting.
• Plotly was used to create interactive visuals that served as supporting tools for the descriptive, diagnostic and prescriptive views, where the focus of each chart was on price structure, assortment breadth or premium risk categories.
• All the code and checks were developed and executed in a notebook environment (Google Colab or Jupyter), thus the analysis and dashboard can be updated whenever new catalogue snapshots are available.

Sources

• Kaggle dataset: ASOS products – scraped ASOS online product listings used to build the ASOS price and assortment view.
• Kaggle dataset: H&M products – scraped H&M online product listings used to build the H&M price and assortment view.
• ASOS official website – used to understand how ASOS presents categories, brands and pricing structure publicly.
• Project documentation and code notebooks stored in a private GitHub repository, including data preparation, analysis scripts and HTML export files.
• No internal ASOS or H&M data, no sales, no ratings; all analysis is based solely on these public catalogue snapshots and may not reflect real‑time prices or promotions.

Technical documentation

Data sources and collection methodology

Public ASOS and H&M product catalogues were downloaded from Kaggle, filtered to online fashion products and cleaned to standardise brand names, prices, currencies and category labels before being merged into a single analysis dataset derived only from these sources.

Tools and technologies used

Python with pandas and numpy was used for data preparation and analysis, Plotly was used to build interactive charts, and the workflow was developed and run in Google Colab or Jupyter Notebook. The final dashboard is delivered as a single HTML file with embedded Plotly.js so it can be viewed fully offline.

Reflection on using AI (ChatGPT)

AI (ChatGPT) was used as a support tool to refine the business question, suggest visual designs, review code structure and improve the clarity of commentary and headings. All datasets, calculations and numeric results come directly from running the Python notebook on the ASOS and H&M catalogue data, not from AI‑generated or synthetic data.

GDPR

This dashboard uses only aggregated, non personal product data scraped from publicly available ASOS and H&M catalogues. No customer, employee or other personal data is collected, stored or processed, and the analysis has been designed to comply with GDPR principles of data minimisation and privacy by design.

Limitations and risks

• The analysis is solely based on the publicly scraped Kaggle catalog data for ASOS and H&M, which means that any missing, duplicated, or misclassified products in these sources will impact the results.
• Only the current online assortments are considered; there is no sales, profit or customer behaviour data, so the findings are more of a positioning description than an actual performance.
• Prices are taken at a single point in time and do not mirror promotions, discount cycles, or stock outs, which may alter customer perception of value.
• Category names were shortened and simplified for the analysis, which may overlook the nuances in how ASOS and H&M internally categorize their products.
• The analysis compares ASOS exclusively with H&M because similar, clean catalog datasets for other major competitors like Zara, Boohoo, or Shein were not consistently available in the required format at the time of the project, hence the results do not reflect ASOS's position across the entire competitive set.

Steps to implementation

• Use this dashboard to align senior stakeholders on how ASOS is currently positioned versus H&M in terms of price bands, assortment breadth and premium exposure.
• Prioritise a small set of focus categories, for example those highlighted in the visuals as high‑price and high‑volume, and run deeper range and pricing diagnostics on those first.
• Combine the catalogue‑level insights with internal data such as sales, margin, inventory and customer segments to test where price or range changes would have the greatest commercial impact.
• Design controlled experiments or pilots, such as adding lower‑priced entry items in selected categories or rebalancing premium options, and track outcomes using updated versions of the dashboard.
• Build a simple refresh process so new ASOS and H&M catalogue snapshots are regularly pulled, cleaned and re‑run through the notebook, turning this into a living monitoring tool rather than a one‑off study.

About

ASOS is a UK‑based online fashion and beauty retailer focused on twenty‑something customers, offering a wide range of own‑brand clothing and third‑party labels across multiple markets through its ecommerce platform.

The company competes with other fast‑ fashion and online players such as H&M, Zara and Boohoo, relying on its digital experience, assortment breadth and brand positioning rather than physical stores.

This dashboard was created as part of the MSc Business Analytics Business Intelligence project to demonstrate how publicly available Kaggle catalogue data for ASOS and H&M can be used to analyse competitive price and assortment positioning.

The work is non‑commercial and intended solely for academic assessment and demonstration of analytical and visualisation skills.